Both SQL and Excel are powerful tools for working with data, but they are designed for different purposes. SQL is a database query language optimized for managing and analyzing large datasets, while Excel is a spreadsheet program suited for more basic data manipulation and analysis. There are pros and cons to each, so which one is “better” depends on your specific needs.
What is SQL?
SQL (Structured Query Language) is a standard programming language used to manage relational databases and perform various operations on the data in them. SQL allows you to write queries to get the exact data you need from a database, insert and edit data, delete records, create and modify database objects like tables, and set permissions for access control. Some common SQL operations include:
- SELECT – Extract data from a database
- UPDATE – Edit data in a database
- DELETE – Delete data from a database
- INSERT – Insert new data into a database
- CREATE – Create new databases, tables, indexes, views, etc
- ALTER – Modify database objects like tables
SQL is an ANSI and ISO standard, so it is compatible across many different relational database management systems like Oracle, MySQL, Microsoft SQL Server, and PostgreSQL. This allows you to leverage your SQL skills across different environments.
What is Excel?
Excel is a spreadsheet program developed by Microsoft for Windows, macOS, Android and iOS. It features calculation, graphing tools, pivot tables, and a macro programming language called Visual Basic for Applications. Excel allows you to store data in a tabular format and manipulate it in many ways.
Key features of Excel include:
- Formulas and functions to perform calculations on data
- PivotTables and PivotCharts for data analysis
- Data visualization tools like charts and graphs
- Ability to organize data in a spreadsheet format with columns and rows
- Macros to automate repetitive tasks
- Collaboration features like managing changes from multiple users
- Powerful data import and transformation capabilities
Excel allows end users to work with data and perform data analysis without needing to know programming languages like SQL.
Key Differences Between SQL and Excel
Here are some of the main differences between SQL and Excel:
| SQL | Excel |
|---|---|
| Designed for managing and querying relational databases | Designed as an electronic spreadsheet program |
| Excellent for structured data management on a large scale | Good for ad-hoc data manipulation and analysis on a smaller scale |
| A standardized language that requires knowledge of syntax and structure | A graphical interface optimized for ease-of-use |
| Capable of handling extremely large datasets | Limited to the number of rows and columns available in Excel |
| Can query and join data from multiple tables | All data must fit into a single spreadsheet |
| Used by database administrators, data analysts, data scientists, etc. | Used by a broader range of end users and business analysts |
| Lets you set user permissions and control access | More limited security and access controls |
When to Use SQL
Here are a few examples of when SQL is the better choice over Excel:
- You need to manage and query very large datasets that won’t fit in an Excel spreadsheet
- You need to work with data contained in multiple relational database tables
- Your data is structured and standardized, not ad-hoc
- You want to automate querying and reporting on a schedule
- Advanced data analytics and transformations are required
- Controlling access and permissions is important
- Data integrity and consistency are critical
- You need to support multiple concurrent users
SQL can handle large structured datasets that would overwhelm Excel. The ability to query and join multiple tables and set user permissions also make SQL the right choice for many business applications.
When to Use Excel
Here are some examples of when Excel is the more appropriate tool compared to SQL:
- You need to do ad-hoc analysis on smaller, undisciplined datasets
- Your data easily fits into a spreadsheet format
- Easy data entry and formatting is required
- You want to quickly visualize data with charts, graphs and pivot tables
- Basic calculations, summing and counting is needed
- You want to leverage Excel’s functions like VLOOKUP, PMT, etc
- No specialized database skills are available
- You need to frequently edit and update data
- Your data doesn’t require rigorous data validation
- You don’t need automation or scheduled reporting
For ad-hoc analysis, visualization and basic data tasks with smaller datasets, Excel offers a user-friendly graphically-oriented platform. The ability to use formulas and quickly edit data make it a versatile end-user tool.
Can SQL and Excel be used together?
Yes, SQL and Excel can complement each other very well. Here are some ways they can work together:
- Use SQL to extract data from a database, then import it into Excel for further analysis
- Use Excel as a simple front-end interface to view and update data from a SQL database
- Manage source data in a SQL database, then build Excel reports and visualizations on top of it
- Use Excel for collecting ad-hoc data, then load it into a SQL database for more robust management
- Employ SQL to clean, validate and process data, then analyze it in Excel
Some key ways to get data between SQL and Excel include ODBC connections, the SQL Server Import and Export Wizard, exporting CSV files, and more. Each tool brings complementary strengths which can enhance the other when used together.
Advantages of SQL over Excel
Here are some key advantages SQL offers over Excel:
- Scalability – SQL databases can store massive amounts of data and serve many concurrent users. Excel files are limited in size and get slow with bigger data.
- Performance – Queries against optimized SQL databases offer much higher performance, especially with large datasets. Excel has performance issues at scale.
- Security – Granular permissions can be applied per user in SQL. Excel has basic worksheet protection but limited security capabilities.
- Concurrency – SQL databases use locking mechanisms allowing many users to access data simultaneously. Multiple users can overwrite changes in Excel.
- Data Integrity – SQL databases enforce constraints, data types, keys and other validation. Excel lacks these safeguards.
- Analytics Capabilities – SQL offers extensive support for analytics functions like window functions, common table expressions, etc. Excel has much more basic analysis features.
For use cases involving huge amounts of structured data that will be used by multiple people, SQL databases provide significant advantages in scalability, performance, security and data integrity.
Advantages of Excel over SQL
Some key advantages Excel offers over raw SQL include:
- User-friendly Interface – Excel provides an intuitive graphical interface optimized for usability. SQL uses a more complex text-based syntax.
- Workflow Integration – Excel integrates well into end-user business workflows. SQL is primarily used by technical roles.
- Visualizations – Powerful charting and graphing capabilities in Excel. Visualizations require technical expertise in SQL.
- Calculations – Formulas, functions and cell references enable complex calculations. SQL can do calculations but Excel provides more built-in convenience.
- Data Editing – Excel makes updating data quicker and easier compared to SQL. Changes require writing UPDATE statements in SQL.
- Ad-hoc Analysis – Excel allows filter, sort and other manipulations not easily achievable in raw SQL. Analysis requires less technical skill.
For ad-hoc analysis involving smaller datasets which doesn’t require rigorous data validation, Excel provides significant ease-of-use advantages.
Conclusion
SQL and Excel are both extremely useful data tools. SQL is better optimized for structured data management at scale, while Excel excels at ad-hoc analysis and visualization for smaller datasets.
Here are some key takeaways:
- SQL can handle large, complex datasets used by multiple concurrent users. Excel has size limitations and performance issues.
- Excel is ideal for end-user data manipulation and visualization. SQL requires more technical skill.
- If data integrity and security are critical, SQL provides much more robust capabilities.
- Excel integrates better into end-user business workflows.
- Excel should be used for smaller analysis tasks. SQL is better for large datasets and production environments.
- SQL and Excel complement each other well. Using them together provides the best of both worlds.
The best approach is to leverage the strengths of both tools. Use SQL to manage master data sources, then Excel for downstream analysis and reporting. Evaluate your specific needs and data environment to determine when to use each tool.
